Title page for etd-0923111-144703


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URN etd-0923111-144703
Author Chia-jung Chen
Author's Email Address No Public.
Statistics This thesis had been viewed 5351 times. Download 125 times.
Department Finance
Year 2011
Semester 1
Degree Master
Type of Document
Language English
Title Pattern Recognition of Technical Analysis Indicators
Date of Defense 2011-06-18
Page Count 57
Keyword
  • trading strategy
  • pattern recognition
  • trend
  • technical analysis
  • encode
  • Abstract In recent years technical analysis has been used more and more frequently. The original concept of technical analysis is built on history will be continue to repeat itself. Therefore, analysts and investors could predict the market price by observing the historical data.
    The idea of pattern recognition technology comes from face recognition systems. In the system, the analyst captures the facial features from the entrant and then quantifies the features as codes. Through the process of recognition, the analyst can confirm the identity of the entrant. Pattern recognition applies the idea to extract information encoded in the stock market characteristics and recognize the market with historical data. In the application, pattern recognition can be regarded as a pre-operation of the technical analysis. Users analyze the current information through pattern recognition and can further build the strategy.
    This model has 19 codes captured from two dimensions; the first is price, and the second is the trend of ups and downs. The empirical results for the decade in the weekly frequency trading strategy are an annual return of 31.57% and annual risk of 26.66%. After the deduction of trading fees, the strategy has an annual return of 14.94% and annual risk of 26.72%.
    Advisory Committee
  • LIN, Chin-Lung - chair
  • TSANG, Shih-Wei - co-chair
  • JHENG, Yi - advisor
  • Files
  • etd-0923111-144703.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2011-09-23

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